There is little research on predictors of patient satisfaction with health care for the 19 million Americans who are diagnosed with major depression and no research on whether these predictors differ by gender for this group. Patient satisfaction is thought to be a key quality of care indicators and is a central health plan performance measurement for the Health Plan Employer Data and Information Set (HEDIS) and the Foundation for Accountability (FACCT). It is conceptualized as combining patient assessments of technical difficulties in distinguishing the two when assessing satisfaction with health care. The main objective of this study is to test whether patient satisfaction is determined by both types of quality of care for people who are diagnosed with major depression and if there are any gender differences in this relationship. In turn, it is also a main objective to understand if provider switching is a consequence of patient dissatisfaction with care. The long-term objective is to help health plans, understand what predicts satisfaction with care so they can target and restructured care, know if there are gender differences in satisfaction, and whether provider switching is a consequence of patient dissatisfaction. The data for this research are from the Quality Improvement for Depression (QID) study, a large national study of 1,481 patients diagnosed with clinical depression in a variety of managed care settings that provides information on patient sociodemographics, health status, visits, attitudes, behavior and satisfaction with health care every six months for a period of 2 years. Data from baseline and six months are available for the following set of specific aims that are not covered for funding under the original QID grant. Specific Aim 1: To determine the effect of quality of care on overall patient satisfaction and patient satisfaction with mental health care. Specific Aim 2: To determine the bi-directional effect of provider switching on patient satisfaction. Specific Aim 3: To develop a comprehensive predictive model of patient satisfaction and determine if predictors differ by gender. These aims lead to a set of hypotheses that are based on the Donabedian Model of Structure-Process-Outcomes and the Andersen Behavioral Model of Health Services Use. The hypotheses of these aims will be tested using multiple logistic regression.